CUSTOM INTEGRATION FRAMEWORK

To effectively manage the data flow, it is essential to implement robust ingestion pipelines. These pipelines must be able to handle both real-time and batch data loading. Ingestion pipelines ensure that data arrives in a timely and orderly manner, ready for further processing. Once collected, the raw data is inserted into the first storage layer, known as the Standard Layer. This layer serves as a temporary repository where data is organized according to the structures defined in the YAML files. The ingestion pipelines, guided by the YAML files, manage both real-time and batch loading, ensuring that data is promptly available for subsequent processing stages.

The framework can integrate with data processing engines (various databases, cloud/on-premise) to perform complex transformations. This layer is responsible for transforming raw data into useful information, applying various cleaning and transformation processes. Additionally, it is implemented to ensure that transformed data always complies with current specifications. SQL models describe the transformation operations to be applied, ensuring that processes are executed consistently and in accordance with business standards.

Data quality is essential to ensure the reliability of analyses. Therefore, the framework includes data validation modules that examine the data to ensure it meets the quality criteria defined in the YAML files. Validation rules may include checks on data types, specific formats, and the removal of duplicates and functional checks. These rules are fully governable and manageable within SQL models, ensuring that validation is consistent and accurate. Monitoring and logging tools are integrated to track transformation operations and quickly identify any issues.

Once transformed, data must be stored in an organized and accessible manner. The framework organizes data into different layers (raw data, processed data, analytics-ready data) to facilitate management and access. The YAML files determine how data should be organized and stored, making access and management easier.

Through the YAML configuration files, the creation of database structures and their evolution over time is automatically managed.

To manage dependencies between different data pipelines and schedule job execution, the framework uses a workflow orchestrator to handle scheduling, retry management in case of failures, and ensure that all parts of the process work in sync.

Automation is a key element to ensure the efficiency and scalability of the framework. A Continuous Integration/Continuous Deployment (CI/CD) pipeline is integrated to automate the deployment and updating of the framework components.